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Record W4410214683 · doi:10.1075/tblt.17.09spa

Reflecting on task-based language teaching from an Instructed SLA perspective

2025· book-chapter· en· W4410214683 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTask-based language teaching · 2025
Typebook-chapter
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPerspective (graphical)Task (project management)PsychologyComputer scienceLinguisticsCognitive psychologyArtificial intelligenceEngineeringPhilosophy

Abstract

fetched live from OpenAlex

Abstract Task-based language teaching (TBLT) and instructed second language acquisition (ISLA) have much in common in terms of theory, research, and educational relevance. The distinguishing characteristic between the two is that TBLT adopts communicative tasks as the central unit for instruction and assessment, whereas ISLA comprises a broader range of instructional activities and assessment practices. In this presentation, I focus on two of the conference themes: Instruction and Outcomes. With respect to Instruction, I draw attention to the pedagogical timing of form-focused instruction (FFI) and corrective feedback. I discuss relevant studies within ISLA and TBLT and argue that TBLT is particularly well-suited to investigating questions about the timing of FFI. In discussing Outcomes, I consider differences in how outcomes are measured in TBLT (i.e., performance) and ISLA (i.e., development) and the different aspects of language examined within each, for example, accuracy, implicit/explicit knowledge in ISLA and complexity, accuracy, and fluency in TBLT. I discuss underlying similarities between fluency and implicit knowledge, how they are measured, and propose research to investigate the pedagogical timing of FFI in relation to fluency development. I conclude with a brief discussion of the need for a balance between theoretically and pedagogically motivated research within ISLA and TBLT.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.702
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0030.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.009
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.326
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it